1. Field of the Invention
The present invention relates to echo-location systems and more specifically pertains to a method of likelihood estimation to obtain optimum performance of echo-location systems in environments of non-Gaussian disturbances such as clutter, reverberation or noise.
2. Description of Related Art
Signal processing in echo-location systems (radar, sonar, laser radar) seeks to obtain accurate estimates of the target reflectivity sequence from the transmitted/received data. The received date includes the effects of environmental disturbances (i.e., ambient noise and propagation effects such as reverberation and clutter) that have a random character, the amount and the statistical characteristics of these environmental disturbances influence the accuracy of the estimated target reflectivity sequence.
Current processing methods are designed to yield optimal estimates for Gaussian environments. The Gaussian (normal) distribution has played a dorminant role in signal processing and often leads to analytically tractable solutions. In many instances of practical interest, however, the statistical environment deviates from the Gaussian assumption. For example, underwater acoustic signals, low frequency atmospheric noise, specular scattering, and many types of man-made noise are found to be non-Gaussian. Performance degradation for systems operating in environments with a significant non-Gaussian component but optimized under the Gaussian assumption, such as the replica correlation (matched filter) processing for coherent reception employed by current radar and sonar systems, leads to an increased false alarm rate with serious practical consequences. The present invention takes the non-Gaussian component of an environment into consideration when estimating target reflectivity. The present invention estimates first the statistics of the prevailing environmental disturbances and then uses those to obtain the optimal estimates of the target reflectivity sequence by means of an adaptive (iterative) estimation algorithm based on the maximization of the corresponding likelihood function.